Mismeasured or missing data can lead to false or misleading conclusions and present a widespread problem in a variety of biomedical fields, including environmental epidemiology. On the other hand, by carefully designing studies to make use of planned missingness, researchers may save money while achieving valid and powerful statistical inference. This project seeks to develop new statistical methods and to apply existing methods to address challenges to valid statistical estimation and testing posed by mismeasured or missing data, both unplanned and planned. - logistic regression, case-control studies, statistical models